In [1]:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import mpl_toolkits as mplot3d
import scipy as sp
pd.set_option("display.max_columns", None)
In [2]:
def lighten_color(color, amount=0.5):
    """
    Lightens the given color by multiplying (1-luminosity) by the given amount.
    Input can be matplotlib color string, hex string, or RGB tuple.

    Examples:
    >> lighten_color('g', 0.3)
    >> lighten_color('#F034A3', 0.6)
    >> lighten_color((.3,.55,.1), 0.5)
    """
    import matplotlib.colors as mc
    import colorsys
    try:
        c = mc.cnames[color]
    except:
        c = color
    c = colorsys.rgb_to_hls(*mc.to_rgb(c))
    return colorsys.hls_to_rgb(c[0], 1 - amount * (1 - c[1]), c[2])
In [3]:
# DF1 = Total and facet scores, and score per question
df1 = pd.read_excel('RisikoAtlas_EuropeanSurvey_randomhalfone.xlsx')
df1
Out[3]:
random responseid country leg_conditions_riskp q0 s1 s2_1 age_rec isced s7 s8 q5_1 q5_2 q5_5 q5_6 q5_9 q8_2_1 q8_2_2 q8_2_3 q8_2_4 q8_2_5 q8_3 q8_4 q8_5berlin_1 q8_5london_1 q8_5paris_1 q8_6 q8_7 q9_1_1 q9_2_1 q9_3 q10_1_1 q10_2_1 q10_3_1 q10_4 q11_1 q12 q13_1 q13_2 q13_3 q13_4 q13_5 q14_1 q14_na q15_1 q16 q17 weight reflectivespending higherincomegroup financialproductclear certainty1 certainty2 certainty3 certainty4 riskcomprehension1 riskcomprehension2 riskcomprehension3 riskcomprehension4 graphliteracy1 graphliteracy2 graphliteracy3 numeracy1 numeracy2 numeracy3 numeracy4 uncertainty1 uncertainty2 uncertainty3 highriskliterate lowriskliterate mediumriskliterate rl_sumscore incomelevels emergencyfund isced_recoded
0 1 2624 10 2 1 1 65 4 2 Vilasantar capataz 4 2 3 4 4 1 1 1 2 2 4 1 1 1 2 2 4 25 60 4 80 1 10.00 2 2 2 1 1 1 0 0 30000.0 0 6 3 1 0.503211 1 0.0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 1 2.0 1 1
1 0 2666 10 1 1 1 61 4 2 LA CORUÑA JUBILADO 3 3 3 5 1 2 2 2 1 2 1 4 2 1 2 4 4 50 66 4 500 10 10.00 2 0 2 1 1 1 0 0 24000.0 0 0 1 4 0.503211 0 0.0 1 1 1 1 0 1 1 0 0 0 0 0 1 1 0 0 1 1 1 0 1 0 7 2.0 0 1
2 1 2891 10 2 1 2 60 4 2 valencia visitador medico 5 3 2 5 5 1 1 1 1 2 2 4 2 1 1 1 1 25 20 3 500 10 0.10 3 0 3 1 1 0 0 0 200000.0 0 1 3 3 0.503211 0 0.0 1 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0 0 1 0 0 0 1 8 3.0 1 1
3 1 2609 10 2 1 1 64 4 2 barcelona ayudante 4 3 2 4 3 1 1 1 1 2 2 2 2 1 1 4 3 50 20 3 500 1000 0.10 2 2 1 1 1 1 0 0 NaN 1 1 3 2 0.503211 1 0.0 0 0 0 0 0 1 1 1 0 0 1 1 1 0 1 0 0 0 0 0 1 0 6 1.0 0 1
4 1 2663 10 3 1 2 70 4 2 virgen del camino Leon Jubilado 5 3 3 4 3 1 1 1 1 2 2 3 2 2 1 1 1 55 20 2 600 5 10.00 1 2 3 1 1 1 1 0 2500.0 0 5 3 1 0.503211 1 0.0 0 0 0 0 0 1 0 1 1 0 1 0 0 0 0 1 0 0 0 0 1 0 4 3.0 1 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1948 0 13378 12 1 1 2 28 2 2 estrées apiculteur 4 3 5 4 5 2 2 2 2 2 4 4 2 1 1 1 1 25 10 3 500 10 10.00 1 1 3 1 1 1 0 0 1000.0 0 2 1 1 1.236911 1 0.0 0 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 0 1 0 1 0 0 10 3.0 1 1
1949 0 13465 12 1 1 2 34 2 2 metz Employé 4 3 3 4 3 1 2 2 2 2 2 2 1 1 1 3 2 18 20 1 500 10 0.10 2 2 5 1 0 0 0 0 10000.0 0 3 1 2 1.236911 1 1.0 0 0 1 1 1 0 1 1 0 0 1 0 1 1 1 0 0 0 0 0 1 0 6 5.0 0 1
1950 0 1122 12 2 1 2 35 2 2 sennecey le grand employé de commerce 4 2 3 4 4 1 1 1 2 2 2 4 2 1 1 1 3 9 10 1 500 10 0.10 3 0 2 1 1 1 0 0 NaN 1 1 2 2 1.236911 1 0.0 0 0 0 0 1 1 1 1 1 0 0 0 1 1 1 0 0 1 0 0 0 1 7 2.0 0 1
1951 1 12807 12 2 1 2 26 2 2 pulnoy jardinier 4 4 4 4 4 1 1 1 2 2 3 3 2 1 1 1 3 8 20 3 500 100 0.01 3 0 4 1 0 0 0 0 NaN 1 0 3 2 1.236911 1 1.0 0 0 0 0 1 1 1 1 1 0 1 1 1 0 0 0 0 0 0 0 1 0 6 4.0 0 1
1952 0 10915 12 2 1 1 34 2 2 cugand asssitante maternelle 3 1 1 3 5 1 1 1 2 2 4 1 1 2 1 4 4 50 20 3 500 10 10.00 2 2 3 1 1 1 1 0 NaN 1 1 2 1 1.236911 1 0.0 0 0 0 0 1 0 0 1 0 0 1 1 1 1 0 0 0 0 1 0 1 0 4 3.0 1 1

1953 rows × 76 columns

In [4]:
df1["age"] = df1["s2_1"]
df1["income"] = df1["q12"]
df1["wealth"] = df1["q14_1"]

df1["Certainty_3"] = df1["certainty1"] + df1["certainty2"] + df1["certainty3"]
df1["RiskComprehension_4"] = df1["riskcomprehension1"] + df1["riskcomprehension1"] + df1["riskcomprehension1"] + df1["riskcomprehension1"]
df1["GraphLiteracy_3"] = df1["graphliteracy1"] + df1["graphliteracy2"] + df1["graphliteracy3"]
df1["Numeracy_3"] = df1["numeracy1"] + df1["numeracy2"] + df1["numeracy3"]
df1["Bayesianreasoning_1"] = df1["numeracy4"]
df1["TotalScore_14"] = df1["Certainty_3"] + df1["RiskComprehension_4"] + df1["GraphLiteracy_3"] + df1["Numeracy_3"] + df1["Bayesianreasoning_1"]

df1["Certainty_%"] = df1["Certainty_3"] / 3 * 100
df1["RiskComprehension_%"] = df1["RiskComprehension_4"] / 4 * 100
df1["GraphLiteracy_%"] = df1["GraphLiteracy_3"] / 3 * 100
df1["Numeracy_%"] = df1["Numeracy_3"] / 3 * 100
df1["Bayesianreasoning_%"] = df1["Bayesianreasoning_1"] / 1 * 100

df1["TotalScore_%"] = df1["TotalScore_14"] / 14 * 100


colReq = ["responseid", "age", "age_rec", "isced", "income", "wealth", "Certainty_3", "RiskComprehension_4", "GraphLiteracy_3",
          "Numeracy_3", "Bayesianreasoning_1", "Certainty_%", "RiskComprehension_%", "GraphLiteracy_%", "Numeracy_%", "Bayesianreasoning_%",
          "TotalScore_14", "TotalScore_%",]

df2 = df1[colReq].copy()
df2
Out[4]:
responseid age age_rec isced income wealth Certainty_3 RiskComprehension_4 GraphLiteracy_3 Numeracy_3 Bayesianreasoning_1 Certainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_14 TotalScore_%
0 2624 65 4 2 2 30000.0 0 0 1 0 0 0.000000 0.0 33.333333 0.000000 0.0 1 7.142857
1 2666 61 4 2 2 24000.0 3 4 0 2 0 100.000000 100.0 0.000000 66.666667 0.0 9 64.285714
2 2891 60 4 2 3 200000.0 0 4 3 3 0 0.000000 100.0 100.000000 100.000000 0.0 10 71.428571
3 2609 64 4 2 1 NaN 0 4 2 2 0 0.000000 100.0 66.666667 66.666667 0.0 8 57.142857
4 2663 70 4 2 3 2500.0 0 4 1 0 1 0.000000 100.0 33.333333 0.000000 100.0 6 42.857143
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1948 13378 28 2 2 3 1000.0 3 4 2 2 1 100.000000 100.0 66.666667 66.666667 100.0 12 85.714286
1949 13465 34 2 2 5 10000.0 2 0 1 3 0 66.666667 0.0 33.333333 100.000000 0.0 6 42.857143
1950 1122 35 2 2 2 NaN 0 4 0 3 0 0.000000 100.0 0.000000 100.000000 0.0 7 50.000000
1951 12807 26 2 2 4 NaN 0 4 2 1 0 0.000000 100.0 66.666667 33.333333 0.0 7 50.000000
1952 10915 34 2 2 3 NaN 0 0 2 2 0 0.000000 0.0 66.666667 66.666667 0.0 4 28.571429

1953 rows × 18 columns

In [5]:
# The following graph represents frequency of each data point on "TotalScore_14" or the total score out of 19 questions across the sample.

df2['TotalScore_14'].plot(kind = 'hist', xticks = np.arange(0, 15, step=1), xlabel = 'TotalScore_14', title = 'Frequency of Scores' )
Out[5]:
<Axes: title={'center': 'Frequency of Scores'}, xlabel='TotalScore_14', ylabel='Frequency'>
No description has been provided for this image
In [6]:
# Absolute mean scores for each facet

(df2[['Certainty_3','RiskComprehension_4','GraphLiteracy_3','Numeracy_3','Bayesianreasoning_1','TotalScore_14']].mean(axis = 0)).plot(kind = 'bar', title = 'Absolute mean of scores for above data set')
Out[6]:
<Axes: title={'center': 'Absolute mean of scores for above data set'}>
No description has been provided for this image
In [7]:
# Normalised mean scores for each facet

df2[['Certainty_%','RiskComprehension_%','GraphLiteracy_%','Numeracy_%','Bayesianreasoning_%','TotalScore_%']].mean(axis = 0).plot(kind = 'bar', title = 'Normalised mean of scores for above data set')
Out[7]:
<Axes: title={'center': 'Normalised mean of scores for above data set'}>
No description has been provided for this image
In [ ]:
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In [8]:
# DF3 = Sorted by income

df3 = df2.sort_values(by = 'income')
df3 = df3.reset_index(drop = True)
df3.drop(df3[df3['income'] == 7].index, inplace = True)

df3
Out[8]:
responseid age age_rec isced income wealth Certainty_3 RiskComprehension_4 GraphLiteracy_3 Numeracy_3 Bayesianreasoning_1 Certainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_14 TotalScore_%
0 3121 25 2 1 1 NaN 0 4 2 3 0 0.000000 100.0 66.666667 100.000000 0.0 9 64.285714
1 922 62 4 3 1 5000.0 1 4 3 2 1 33.333333 100.0 100.000000 66.666667 100.0 11 78.571429
2 9780 32 2 2 1 600.0 0 4 1 1 0 0.000000 100.0 33.333333 33.333333 0.0 6 42.857143
3 5235 26 2 1 1 1000.0 0 4 1 1 0 0.000000 100.0 33.333333 33.333333 0.0 6 42.857143
4 6429 25 2 1 1 5300.0 1 0 1 1 0 33.333333 0.0 33.333333 33.333333 0.0 3 21.428571
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1695 330 53 3 1 6 5000.0 1 4 2 3 0 33.333333 100.0 66.666667 100.000000 0.0 10 71.428571
1696 9132 55 3 1 6 NaN 0 0 2 0 0 0.000000 0.0 66.666667 0.000000 0.0 2 14.285714
1697 27 46 3 1 6 500000.0 3 4 3 3 0 100.000000 100.0 100.000000 100.000000 0.0 13 92.857143
1698 8715 33 2 2 6 3500000.0 0 0 1 1 0 0.000000 0.0 33.333333 33.333333 0.0 2 14.285714
1699 583 39 3 1 6 NaN 0 0 3 3 1 0.000000 0.0 100.000000 100.000000 100.0 7 50.000000

1700 rows × 18 columns

In [9]:
# Absolute mean score for each facet (Sort by Income)

df3[['Certainty_3','RiskComprehension_4','GraphLiteracy_3','Numeracy_3','Bayesianreasoning_1','TotalScore_14']].mean(axis = 0).plot(kind = 'bar', title = 'Absolute mean score for each facet (Sort by Income)')
Out[9]:
<Axes: title={'center': 'Absolute mean score for each facet (Sort by Income)'}>
No description has been provided for this image
In [10]:
# Normalised mean score for each facet (Sort by Income)

(df3[['Certainty_%','RiskComprehension_%','GraphLiteracy_%','Numeracy_%','Bayesianreasoning_%','TotalScore_%']].mean(axis = 0)).plot(kind = 'bar', title = 'Normalised mean score for each facet (Sort by Income)')
Out[10]:
<Axes: title={'center': 'Normalised mean score for each facet (Sort by Income)'}>
No description has been provided for this image
In [11]:
# Normalised mean of each facet as a numerical (sorted by income)

df3.groupby('income')[['Certainty_%','RiskComprehension_%','GraphLiteracy_%','Numeracy_%','Bayesianreasoning_%','TotalScore_%']].mean()
Out[11]:
Certainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_%
income
1 20.495495 62.612613 55.780781 49.474474 20.045045 46.267696
2 22.222222 70.959596 61.026936 57.659933 15.404040 51.569264
3 23.937360 71.812081 61.744966 60.514541 19.798658 53.259827
4 25.941423 80.334728 59.972106 62.761506 20.083682 56.246264
5 24.113475 76.595745 63.475177 67.907801 19.680851 56.610942
6 33.333333 79.259259 63.209877 69.135802 19.259259 59.523810
In [12]:
# Count of responses for each category (sorted by income)

df3.groupby('income')[['responseid']].count()
Out[12]:
responseid
income
1 444
2 396
3 298
4 239
5 188
6 135
In [13]:
# Absolute mean of Total Facet score line plot (sorted by income)

df3.groupby('income')[['TotalScore_14']].mean().plot( kind = 'line', title = 'Absolute mean of Total Facet score line plot (sorted by income)').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[13]:
<matplotlib.legend.Legend at 0x26c4a151410>
No description has been provided for this image
In [14]:
# Absolute mean of each Facet score line plot (sorted by income)

df3.groupby('income')[['Certainty_3','RiskComprehension_4','GraphLiteracy_3','Numeracy_3','Bayesianreasoning_1']].mean().plot( kind = 'line', title = 'Absolute Facet scores mean line plot').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[14]:
<matplotlib.legend.Legend at 0x26c49ef4650>
No description has been provided for this image
In [15]:
# Normalised mean of each Facet score line plot (sorted by income)


df3.groupby('income')[['Certainty_%','RiskComprehension_%','GraphLiteracy_%','Numeracy_%','Bayesianreasoning_%', 'TotalScore_%']].mean().plot( kind = 'line', title = 'Normalised mean of each Facet score line plot (sorted by income)').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[15]:
<matplotlib.legend.Legend at 0x26c4a4955d0>
No description has been provided for this image
In [16]:
# Normalised mean of each Facet score stacked plot (sorted by income)

df3.groupby('income')[['Certainty_%','RiskComprehension_%','GraphLiteracy_%','Numeracy_%','Bayesianreasoning_%']].mean().plot( kind = 'bar', title = 'Normalised mean of each Facet score stacked plot (sorted by income)', stacked = True).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[16]:
<matplotlib.legend.Legend at 0x26c49e272d0>
No description has been provided for this image
In [17]:
# Normalised mean of each Facet score hist plot (sorted by income)

df3.groupby('income')[['Certainty_%','RiskComprehension_%','GraphLiteracy_%','Numeracy_%','Bayesianreasoning_%','TotalScore_%']].mean().T.plot(kind = 'bar', title = 'Normalised mean scores for each facet for each wealth response category').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[17]:
<matplotlib.legend.Legend at 0x26c4a51a550>
No description has been provided for this image
In [18]:
# Trend line for Absolute Total Facet Score vs income

sns.regplot (data = df3, x = 'income', y = 'TotalScore_14')
Out[18]:
<Axes: xlabel='income', ylabel='TotalScore_14'>
No description has been provided for this image
In [19]:
# Trend line for Absolute Independent Facet Score vs income reponses

fig, ax = plt.subplots(figsize=(6, 6))

sns.regplot (data = df3, x = 'income', y = 'Certainty_3', fit_reg=True, ci=None, ax=ax, label='Certainty_3')
sns.regplot (data = df3, x = 'income', y = 'RiskComprehension_4', fit_reg=True, ci=None, ax=ax, label='RiskComprehension_4')
sns.regplot (data = df3, x = 'income', y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax, label='Numeracy_3')
sns.regplot (data = df3, x = 'income', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax, label='GraphLiteracy_3')
sns.regplot (data = df3, x = 'income', y = 'Bayesianreasoning_1',fit_reg=True, ci=None, ax=ax, label='Bayesianreasoning_1' )

ax.set(ylabel='Scores', xlabel='income')
ax.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [20]:
# Violine Plot for TotalScore_14 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'TotalScore_14')
Out[20]:
<Axes: xlabel='income', ylabel='TotalScore_14'>
No description has been provided for this image
In [21]:
# Violine Plot for Certainty_3 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'Certainty_3')
Out[21]:
<Axes: xlabel='income', ylabel='Certainty_3'>
No description has been provided for this image
In [22]:
# Violine Plot for RiskComprehension_4 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'RiskComprehension_4')
Out[22]:
<Axes: xlabel='income', ylabel='RiskComprehension_4'>
No description has been provided for this image
In [23]:
# Violine Plot for GraphLiteracy_3 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'GraphLiteracy_3')
Out[23]:
<Axes: xlabel='income', ylabel='GraphLiteracy_3'>
No description has been provided for this image
In [24]:
# Violine Plot for Numeracy_3 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'Numeracy_3')
Out[24]:
<Axes: xlabel='income', ylabel='Numeracy_3'>
No description has been provided for this image
In [25]:
# Violine Plot for TotalScore_14 for each entry under income.

sns.violinplot( data = df3, x = 'income', y = 'Bayesianreasoning_1')
Out[25]:
<Axes: xlabel='income', ylabel='Bayesianreasoning_1'>
No description has been provided for this image
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In [26]:
# Data Frame 2 or df2 is an aggregate data on facet total scores and over all total score, along with wealth and income data vs each response. 
# This data set is also sorted by wealth with all responses 'NA' removed.
# We also assign quartiles ranking each response according to this sort.

df4 = df2.sort_values(by = 'wealth')
df4 = df4.reset_index(drop = True)
df4 = df4.dropna(axis = 0, subset = 'wealth')
df4.insert(loc = len(df4.columns), column = "Quartile Number", value = pd.qcut(df4["wealth"],q = 4, labels = False ) + 1, allow_duplicates = 'False')

df4
Out[26]:
responseid age age_rec isced income wealth Certainty_3 RiskComprehension_4 GraphLiteracy_3 Numeracy_3 Bayesianreasoning_1 Certainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_14 TotalScore_% Quartile Number
0 7204 57 3 2 1 0.0 0 4 3 2 0 0.000000 100.0 100.000000 66.666667 0.0 9 64.285714 1
1 1076 39 3 3 2 0.0 3 4 3 2 0 100.000000 100.0 100.000000 66.666667 0.0 12 85.714286 1
2 568 71 4 3 6 0.0 0 0 0 1 0 0.000000 0.0 0.000000 33.333333 0.0 1 7.142857 1
3 262 62 4 2 2 0.0 0 4 2 2 0 0.000000 100.0 66.666667 66.666667 0.0 8 57.142857 1
4 887 70 4 3 1 0.0 1 4 2 2 0 33.333333 100.0 66.666667 66.666667 0.0 9 64.285714 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1117 3160 49 3 1 4 4500000.0 1 4 2 2 0 33.333333 100.0 66.666667 66.666667 0.0 9 64.285714 4
1118 1519 71 4 3 2 5000000.0 1 4 0 0 0 33.333333 100.0 0.000000 0.000000 0.0 5 35.714286 4
1119 6471 71 4 2 3 5000000.0 0 4 2 3 0 0.000000 100.0 66.666667 100.000000 0.0 9 64.285714 4
1120 97 24 2 1 4 5000000.0 3 4 2 0 1 100.000000 100.0 66.666667 0.000000 100.0 10 71.428571 4
1121 706 28 2 3 1 5000000.0 3 4 2 3 0 100.000000 100.0 66.666667 100.000000 0.0 12 85.714286 4

1122 rows × 19 columns

In [27]:
# Absolute mean score for each facet (Sort by Wealth)

df4[['Certainty_3','RiskComprehension_4','Numeracy_3','GraphLiteracy_3','Bayesianreasoning_1','TotalScore_14']].mean(axis = 0).plot(kind = 'bar', title = 'Absolute mean score for each facet (Sort by Wealth)')
Out[27]:
<Axes: title={'center': 'Absolute mean score for each facet (Sort by Wealth)'}>
No description has been provided for this image
In [28]:
# Normalised mean score for each facet (Sort by Wealth)

(df4[['Certainty_%','RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%','TotalScore_%']].mean(axis = 0)).plot(kind = 'bar', title = 'Normalised mean score for each facet (Sort by Wealth)')
Out[28]:
<Axes: title={'center': 'Normalised mean score for each facet (Sort by Wealth)'}>
No description has been provided for this image
In [29]:
# Line Plot of Absolute Total Facet score vs sorted wealth

df4.plot(use_index = 'True' , kind = 'line', y = ['TotalScore_14'], title = 'Total responses line plot sorted by Wealth',xlabel = 'Unique responses sorted by wealth', ylabel = 'TotalScore_14')
Out[29]:
<Axes: title={'center': 'Total responses line plot sorted by Wealth'}, xlabel='Unique responses sorted by wealth', ylabel='TotalScore_14'>
No description has been provided for this image
In [30]:
# Absolute Facet scores line plot from low to high wealth 
# I do understand that this is non readable

df4[['Certainty_3','RiskComprehension_4','Numeracy_3','GraphLiteracy_3','Bayesianreasoning_1']].plot( kind = 'line', title = 'Absolute Facet scores line plot', use_index = True).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[30]:
<matplotlib.legend.Legend at 0x26c4ca3a590>
No description has been provided for this image
In [31]:
# Trend line for Absolute Total Facet Score vs unique wealth reponses

sns.regplot (data = df4, x = df4.index, y = 'TotalScore_14')
Out[31]:
<Axes: ylabel='TotalScore_14'>
No description has been provided for this image
In [32]:
# Trend line for Absolute Independent Facet Score vs wealth reponses

fig1, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4, x = df4.index, y = 'Certainty_3', fit_reg=True, ci=None, ax=ax1, label='Certainty_3')
sns.regplot (data = df4, x = df4.index, y = 'RiskComprehension_4', fit_reg=True, ci=None, ax=ax1, label='RiskComprehension_4')
sns.regplot (data = df4, x = df4.index, y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax1, label='Numeracy_3')
sns.regplot (data = df4, x = df4.index, y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax1, label='GraphLiteracy_3')
sns.regplot (data = df4, x = df4.index, y = 'Bayesianreasoning_1',fit_reg=True, ci=None, ax=ax1, label='Bayesianreasoning_1' )

ax1.set(ylabel='Scores', xlabel='wealth')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [33]:
# Count of number of responses under each quartile

df4.groupby('Quartile Number')[['Quartile Number']].count()
Out[33]:
Quartile Number
Quartile Number
1 290
2 271
3 282
4 279
In [34]:
df4.loc[df4['Quartile Number']==1]
Out[34]:
responseid age age_rec isced income wealth Certainty_3 RiskComprehension_4 GraphLiteracy_3 Numeracy_3 Bayesianreasoning_1 Certainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_14 TotalScore_% Quartile Number
0 7204 57 3 2 1 0.0 0 4 3 2 0 0.000000 100.0 100.000000 66.666667 0.0 9 64.285714 1
1 1076 39 3 3 2 0.0 3 4 3 2 0 100.000000 100.0 100.000000 66.666667 0.0 12 85.714286 1
2 568 71 4 3 6 0.0 0 0 0 1 0 0.000000 0.0 0.000000 33.333333 0.0 1 7.142857 1
3 262 62 4 2 2 0.0 0 4 2 2 0 0.000000 100.0 66.666667 66.666667 0.0 8 57.142857 1
4 887 70 4 3 1 0.0 1 4 2 2 0 33.333333 100.0 66.666667 66.666667 0.0 9 64.285714 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
285 9763 31 2 1 5 2000.0 1 4 3 1 0 33.333333 100.0 100.000000 33.333333 0.0 9 64.285714 1
286 125 37 3 2 4 2000.0 1 0 2 1 0 33.333333 0.0 66.666667 33.333333 0.0 4 28.571429 1
287 639 72 4 3 2 2000.0 0 4 2 3 0 0.000000 100.0 66.666667 100.000000 0.0 9 64.285714 1
288 648 62 4 3 2 2000.0 0 0 3 1 0 0.000000 0.0 100.000000 33.333333 0.0 4 28.571429 1
289 8119 39 3 2 3 2000.0 0 4 1 2 0 0.000000 100.0 33.333333 66.666667 0.0 7 50.000000 1

290 rows × 19 columns

In [35]:
# This graph is basically a line plot of the TotalScore_14 across first quartile unique responses which are ordered by income

df4.loc[df4['Quartile Number']==1].plot(use_index = 'True' , kind = 'line', y = ['TotalScore_14'], title = 'Total Score line plot sorted by Wealth Q1',xlabel = 'Unique responses', ylabel = 'TotalScore_14')
Out[35]:
<Axes: title={'center': 'Total Score line plot sorted by Wealth Q1'}, xlabel='Unique responses', ylabel='TotalScore_14'>
No description has been provided for this image
In [36]:
# Trend line for Absolute Total Facet Score vs unique Q14. Income reponses in Quartile 1

sns.regplot (data = df4.loc[df4['Quartile Number']==1], x = df4.loc[df4['Quartile Number']==1].index, y = 'TotalScore_14')
Out[36]:
<Axes: ylabel='TotalScore_14'>
No description has been provided for this image
In [37]:
# Trend line for Absolute Independent Facet Score vs wealth reponses in Quartile 1

fig1, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4.loc[df4['Quartile Number']==1], x = df4.loc[df4['Quartile Number']==1].index, y = 'Certainty_3', fit_reg=True, ci=None, ax=ax2, label='Certainty_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==1], x = df4.loc[df4['Quartile Number']==1].index, y = 'RiskComprehension_4', fit_reg=True, ci=None, ax=ax2, label='RiskComprehension_4')
sns.regplot (data = df4.loc[df4['Quartile Number']==1], x = df4.loc[df4['Quartile Number']==1].index, y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax2, label='Numeracy_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==1], x = df4.loc[df4['Quartile Number']==1].index, y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax2, label='GraphLiteracy_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==1], x = df4.loc[df4['Quartile Number']==1].index, y = 'Bayesianreasoning_1',fit_reg=True, ci=None, ax=ax2, label='Bayesianreasoning_1' )

ax2.set(ylabel='Scores', xlabel='wealth')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [ ]:
In [38]:
df4.loc[df4['Quartile Number']==2]
Out[38]:
responseid age age_rec isced income wealth Certainty_3 RiskComprehension_4 GraphLiteracy_3 Numeracy_3 Bayesianreasoning_1 Certainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_14 TotalScore_% Quartile Number
290 11209 21 2 1 2 2500.0 1 4 0 0 0 33.333333 100.0 0.000000 0.000000 0.0 5 35.714286 2
291 8599 42 3 3 1 2500.0 1 0 3 1 0 33.333333 0.0 100.000000 33.333333 0.0 5 35.714286 2
292 6060 33 2 1 3 2500.0 0 4 1 2 0 0.000000 100.0 33.333333 66.666667 0.0 7 50.000000 2
293 2663 70 4 2 3 2500.0 0 4 1 0 1 0.000000 100.0 33.333333 0.000000 100.0 6 42.857143 2
294 1389 49 3 2 1 2500.0 2 4 3 2 0 66.666667 100.0 100.000000 66.666667 0.0 11 78.571429 2
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
556 5673 39 3 1 4 40000.0 1 4 3 2 1 33.333333 100.0 100.000000 66.666667 100.0 11 78.571429 2
557 935 56 3 2 3 40000.0 0 4 3 1 0 0.000000 100.0 100.000000 33.333333 0.0 8 57.142857 2
558 1144 55 3 2 4 40000.0 1 4 1 3 0 33.333333 100.0 33.333333 100.000000 0.0 9 64.285714 2
559 2257 56 3 2 1 42000.0 1 4 3 2 0 33.333333 100.0 100.000000 66.666667 0.0 10 71.428571 2
560 5524 41 3 2 4 42000.0 0 4 3 2 0 0.000000 100.0 100.000000 66.666667 0.0 9 64.285714 2

271 rows × 19 columns

In [39]:
# This graph is basically a line plot of the TotalScore_14 across second quartile unique responses which are ordered by income

df4.loc[df4['Quartile Number']==2].plot(use_index = 'True' , kind = 'line', y = ['TotalScore_14'], title = 'Total Score line plot sorted by Wealth Q1',xlabel = 'Unique responses', ylabel = 'TotalScore_14')
Out[39]:
<Axes: title={'center': 'Total Score line plot sorted by Wealth Q1'}, xlabel='Unique responses', ylabel='TotalScore_14'>
No description has been provided for this image
In [40]:
# Trend line for Absolute Total Facet Score vs unique wealth reponses in Quartile 2

sns.regplot (data = df4.loc[df4['Quartile Number']==2], x = df4.loc[df4['Quartile Number']==2].index, y = 'TotalScore_14')
Out[40]:
<Axes: ylabel='TotalScore_14'>
No description has been provided for this image
In [41]:
# Trend line for Absolute Independent Facet Score vs wealth reponses in Quartile 2

fig1, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4.loc[df4['Quartile Number']==2], x = df4.loc[df4['Quartile Number']==2].index, y = 'Certainty_3', fit_reg=True, ci=None, ax=ax3, label='Certainty_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==2], x = df4.loc[df4['Quartile Number']==2].index, y = 'RiskComprehension_4', fit_reg=True, ci=None, ax=ax3, label='RiskComprehension_4')
sns.regplot (data = df4.loc[df4['Quartile Number']==2], x = df4.loc[df4['Quartile Number']==2].index, y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax3, label='Numeracy_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==2], x = df4.loc[df4['Quartile Number']==2].index, y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax3, label='GraphLiteracy_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==2], x = df4.loc[df4['Quartile Number']==2].index, y = 'Bayesianreasoning_1',fit_reg=True, ci=None, ax=ax3, label='Bayesianreasoning_1' )

ax3.set(ylabel='Scores', xlabel='wealth')
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [ ]:
In [42]:
df4.loc[df4['Quartile Number']==3]
Out[42]:
responseid age age_rec isced income wealth Certainty_3 RiskComprehension_4 GraphLiteracy_3 Numeracy_3 Bayesianreasoning_1 Certainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_14 TotalScore_% Quartile Number
561 9500 75 4 2 7 43654.0 1 4 1 0 0 33.333333 100.0 33.333333 0.000000 0.0 6 42.857143 3
562 711 61 4 2 3 45000.0 0 0 2 3 0 0.000000 0.0 66.666667 100.000000 0.0 5 35.714286 3
563 5337 26 2 1 3 45000.0 0 4 1 2 0 0.000000 100.0 33.333333 66.666667 0.0 7 50.000000 3
564 11018 33 2 1 2 50000.0 2 4 1 3 0 66.666667 100.0 33.333333 100.000000 0.0 10 71.428571 3
565 2094 55 3 2 3 50000.0 3 4 3 3 0 100.000000 100.0 100.000000 100.000000 0.0 13 92.857143 3
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
838 6522 23 2 3 5 200000.0 1 4 0 0 0 33.333333 100.0 0.000000 0.000000 0.0 5 35.714286 3
839 10125 66 4 2 7 200000.0 0 4 3 2 0 0.000000 100.0 100.000000 66.666667 0.0 9 64.285714 3
840 9017 63 4 2 1 200000.0 1 0 2 2 1 33.333333 0.0 66.666667 66.666667 100.0 6 42.857143 3
841 8754 52 3 2 1 200000.0 0 4 2 3 0 0.000000 100.0 66.666667 100.000000 0.0 9 64.285714 3
842 2831 42 3 2 4 200000.0 2 0 0 1 1 66.666667 0.0 0.000000 33.333333 100.0 4 28.571429 3

282 rows × 19 columns

In [43]:
# This graph is basically a line plot of the TotalScore_14 across third quartile unique responses which are ordered by wealth

df4.loc[df4['Quartile Number']==3].plot(use_index = 'True' , kind = 'line', y = ['TotalScore_14'], title = 'Total Score line plot sorted by Wealth Q3',xlabel = 'Unique responses', ylabel = 'TotalScore_14')
Out[43]:
<Axes: title={'center': 'Total Score line plot sorted by Wealth Q3'}, xlabel='Unique responses', ylabel='TotalScore_14'>
No description has been provided for this image
In [44]:
# Trend line for Absolute Total Facet Score vs unique wealth reponses in Quartile 3

sns.regplot (data = df4.loc[df4['Quartile Number']==3], x = df4.loc[df4['Quartile Number']==3].index, y = 'TotalScore_14')
Out[44]:
<Axes: ylabel='TotalScore_14'>
No description has been provided for this image
In [45]:
# Trend line for Absolute Independent Facet Score vs wealth reponses in Quartile 3

fig1, ax4 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4.loc[df4['Quartile Number']==3], x = df4.loc[df4['Quartile Number']==3].index, y = 'Certainty_3', fit_reg=True, ci=None, ax=ax4, label='Certainty_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==3], x = df4.loc[df4['Quartile Number']==3].index, y = 'RiskComprehension_4', fit_reg=True, ci=None, ax=ax4, label='RiskComprehension_4')
sns.regplot (data = df4.loc[df4['Quartile Number']==3], x = df4.loc[df4['Quartile Number']==3].index, y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax4, label='Numeracy_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==3], x = df4.loc[df4['Quartile Number']==3].index, y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax4, label='GraphLiteracy_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==3], x = df4.loc[df4['Quartile Number']==3].index, y = 'Bayesianreasoning_1',fit_reg=True, ci=None, ax=ax4, label='Bayesianreasoning_1' )

ax4.set(ylabel='Scores', xlabel='wealth')
ax4.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [ ]:
In [46]:
df4.loc[df4['Quartile Number']==4]
Out[46]:
responseid age age_rec isced income wealth Certainty_3 RiskComprehension_4 GraphLiteracy_3 Numeracy_3 Bayesianreasoning_1 Certainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_14 TotalScore_% Quartile Number
843 5864 48 3 1 4 220000.0 0 4 2 3 0 0.000000 100.0 66.666667 100.000000 0.0 9 64.285714 4
844 8570 25 2 1 6 220000.0 0 4 1 3 0 0.000000 100.0 33.333333 100.000000 0.0 8 57.142857 4
845 2623 59 3 1 2 220000.0 1 4 2 3 0 33.333333 100.0 66.666667 100.000000 0.0 10 71.428571 4
846 13145 61 4 2 2 220000.0 0 4 1 0 0 0.000000 100.0 33.333333 0.000000 0.0 5 35.714286 4
847 10672 63 4 2 6 250000.0 0 4 1 2 0 0.000000 100.0 33.333333 66.666667 0.0 7 50.000000 4
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1117 3160 49 3 1 4 4500000.0 1 4 2 2 0 33.333333 100.0 66.666667 66.666667 0.0 9 64.285714 4
1118 1519 71 4 3 2 5000000.0 1 4 0 0 0 33.333333 100.0 0.000000 0.000000 0.0 5 35.714286 4
1119 6471 71 4 2 3 5000000.0 0 4 2 3 0 0.000000 100.0 66.666667 100.000000 0.0 9 64.285714 4
1120 97 24 2 1 4 5000000.0 3 4 2 0 1 100.000000 100.0 66.666667 0.000000 100.0 10 71.428571 4
1121 706 28 2 3 1 5000000.0 3 4 2 3 0 100.000000 100.0 66.666667 100.000000 0.0 12 85.714286 4

279 rows × 19 columns

In [47]:
# This graph is basically a line plot of the TotalScore_14 across fourth quartile unique responses which are ordered by Wealth

df4.loc[df4['Quartile Number']==4].plot(use_index = 'True' , kind = 'line', y = ['TotalScore_14'], title = 'Total Score line plot sorted by Wealth Q4',xlabel = 'Unique responses', ylabel = 'TotalScore_14')
Out[47]:
<Axes: title={'center': 'Total Score line plot sorted by Wealth Q4'}, xlabel='Unique responses', ylabel='TotalScore_14'>
No description has been provided for this image
In [48]:
# Trend line for Absolute Total Facet Score vs unique wealth reponses in Quartile 4

sns.regplot (data = df4.loc[df4['Quartile Number']==4], x = df4.loc[df4['Quartile Number']==4].index, y = 'TotalScore_14')
Out[48]:
<Axes: ylabel='TotalScore_14'>
No description has been provided for this image
In [49]:
# Trend line for Absolute Independent Facet Score vs wealth reponses in Quartile 4

fig1, ax5 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4.loc[df4['Quartile Number']==4], x = df4.loc[df4['Quartile Number']==4].index, y = 'Certainty_3', fit_reg=True, ci=None, ax=ax5, label='Certainty_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==4], x = df4.loc[df4['Quartile Number']==4].index, y = 'RiskComprehension_4', fit_reg=True, ci=None, ax=ax5, label='RiskComprehension_4')
sns.regplot (data = df4.loc[df4['Quartile Number']==4], x = df4.loc[df4['Quartile Number']==4].index, y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax5, label='Numeracy_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==4], x = df4.loc[df4['Quartile Number']==4].index, y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax5, label='GraphLiteracy_3')
sns.regplot (data = df4.loc[df4['Quartile Number']==4], x = df4.loc[df4['Quartile Number']==4].index, y = 'Bayesianreasoning_1',fit_reg=True, ci=None, ax=ax5, label='Bayesianreasoning_1' )

ax5.set(ylabel='Scores', xlabel='wealth')
ax5.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [ ]:
In [ ]:
In [50]:
# Absolute Total Facet scores mean line plot from low to high Wealth

df4.groupby('Quartile Number')[['TotalScore_14']].mean().plot( kind = 'line', title = 'Absolute Total Facet scores mean line plot low to high wealth', xticks = np.arange(1,5,step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[50]:
<matplotlib.legend.Legend at 0x26c50429090>
No description has been provided for this image
In [51]:
# Absolute Facet scores mean line plot from low to high wealth

df4.groupby('Quartile Number')[['Certainty_3','RiskComprehension_4','Numeracy_3','GraphLiteracy_3','Bayesianreasoning_1']].mean().plot( kind = 'line', title = 'Absolute Facet scores mean line plot low to high wealth', xticks = np.arange(1,5,step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[51]:
<matplotlib.legend.Legend at 0x26c4efc5b50>
No description has been provided for this image
In [52]:
# Normalised Facet scores mean line plot from low to high wealth


df4.groupby('Quartile Number')[['Certainty_%','RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%','TotalScore_%']].mean().plot( kind = 'line', title = 'Normalised Facet scores mean line plot from low to high wealth',  xticks = np.arange(1,5,step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[52]:
<matplotlib.legend.Legend at 0x26c506e8850>
No description has been provided for this image
In [53]:
# Normalised mean scores for each facet stacked

df4.groupby('Quartile Number')[['Certainty_%','RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%']].mean().plot( kind = 'bar', title = 'Normalised mean scores for each facet stacked', stacked = True).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[53]:
<matplotlib.legend.Legend at 0x26c5085af50>
No description has been provided for this image
In [54]:
# Normalised mean scores for each facet for each wealth response Quartile

df4.groupby('Quartile Number')[['Certainty_%','RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%','TotalScore_%']].mean().T.plot(kind = 'bar', title = 'Normalised mean scores for each facet for each wealth response category').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[54]:
<matplotlib.legend.Legend at 0x26c5040f9d0>
No description has been provided for this image
In [55]:
# Trend line for Absolute Total Facet Score vs Quartiles

sns.regplot (data = df4, x = 'Quartile Number', y = 'TotalScore_14')
Out[55]:
<Axes: xlabel='Quartile Number', ylabel='TotalScore_14'>
No description has been provided for this image
In [56]:
# Trend line for Absolute Independent Facet Score vs Quartiles

fig2, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df4, x = 'Quartile Number', y = 'Certainty_3', fit_reg=True, ci=None, ax=ax2, label='Certainty_3')
sns.regplot (data = df4, x = 'Quartile Number', y = 'RiskComprehension_4', fit_reg=True, ci=None, ax=ax2, label='RiskComprehension_4')
sns.regplot (data = df4, x = 'Quartile Number', y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax2, label='Numeracy_3')
sns.regplot (data = df4, x = 'Quartile Number', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax2, label='GraphLiteracy_3')
sns.regplot (data = df4, x = 'Quartile Number', y = 'Bayesianreasoning_1',fit_reg=True, ci=None, ax=ax2, label='Bayesianreasoning_1' )

ax2.set(ylabel='Scores', xlabel='wealth')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [57]:
# Violine Plot for TotalScore_14 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'TotalScore_14')
Out[57]:
<Axes: xlabel='Quartile Number', ylabel='TotalScore_14'>
No description has been provided for this image
In [58]:
# Violine Plot for Certainty_3 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'Certainty_3')
Out[58]:
<Axes: xlabel='Quartile Number', ylabel='Certainty_3'>
No description has been provided for this image
In [59]:
# Violine Plot for RiskComprehension_4 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'RiskComprehension_4')
Out[59]:
<Axes: xlabel='Quartile Number', ylabel='RiskComprehension_4'>
No description has been provided for this image
In [60]:
# Violine Plot for GraphLiteracy_3 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'GraphLiteracy_3')
Out[60]:
<Axes: xlabel='Quartile Number', ylabel='GraphLiteracy_3'>
No description has been provided for this image
In [61]:
# Violine Plot for Numeracy_3 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'Numeracy_3')
Out[61]:
<Axes: xlabel='Quartile Number', ylabel='Numeracy_3'>
No description has been provided for this image
In [62]:
# Violine Plot for Bayesianreasoning_1 for each entry under Wealth Quartiles.

sns.violinplot( data = df4, x = 'Quartile Number', y = 'Bayesianreasoning_1')
Out[62]:
<Axes: xlabel='Quartile Number', ylabel='Bayesianreasoning_1'>
No description has been provided for this image
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [63]:
# Data Frame 5 or df5 is an aggregate data on facet total scores and over all total score, along with  education data vs each response. 
# This data set is now sorted by education.

df5 = df2.sort_values(by = 'isced')
df5 = df5.reset_index(drop = True)

df5
Out[63]:
responseid age age_rec isced income wealth Certainty_3 RiskComprehension_4 GraphLiteracy_3 Numeracy_3 Bayesianreasoning_1 Certainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_14 TotalScore_%
0 10237 42 3 1 6 50000.0 1 4 2 3 0 33.333333 100.0 66.666667 100.000000 0.0 10 71.428571
1 32 31 2 1 2 NaN 2 4 2 3 0 66.666667 100.0 66.666667 100.000000 0.0 11 78.571429
2 5234 28 2 1 2 NaN 0 4 2 3 0 0.000000 100.0 66.666667 100.000000 0.0 9 64.285714
3 118 33 2 1 1 120000.0 0 0 1 0 0 0.000000 0.0 33.333333 0.000000 0.0 1 7.142857
4 2842 26 2 1 3 200000.0 0 4 2 3 0 0.000000 100.0 66.666667 100.000000 0.0 9 64.285714
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1948 1456 36 3 3 1 NaN 2 4 2 2 1 66.666667 100.0 66.666667 66.666667 100.0 11 78.571429
1949 2846 47 3 3 2 200000.0 0 0 1 0 0 0.000000 0.0 33.333333 0.000000 0.0 1 7.142857
1950 8284 38 3 3 1 450000.0 0 0 1 0 0 0.000000 0.0 33.333333 0.000000 0.0 1 7.142857
1951 8253 50 3 3 1 0.0 0 0 1 2 1 0.000000 0.0 33.333333 66.666667 100.0 4 28.571429
1952 2671 73 4 3 3 1200.0 0 4 3 1 0 0.000000 100.0 100.000000 33.333333 0.0 8 57.142857

1953 rows × 18 columns

In [64]:
# since we didnt drop any values, the dataset is essentially the same as df2, but sorted
In [65]:
# Mean of each facet as a % for each category of isced

df5.groupby('isced')[['Certainty_%','RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%']].mean()
Out[65]:
Certainty_% RiskComprehension_% Numeracy_% GraphLiteracy_% Bayesianreasoning_%
isced
1 28.601144 78.783151 69.006760 66.146646 21.840874
2 21.284185 69.084423 56.916369 60.007927 17.241379
3 22.080679 65.605096 47.629158 51.026185 17.409766
In [66]:
# Count of responses for each category isced

df5.groupby('isced')[['responseid']].count()
Out[66]:
responseid
isced
1 641
2 841
3 471
In [67]:
# Absolute Total Facet scores mean line plot from low to high education

df5.groupby('isced')[['TotalScore_14']].mean().plot( kind = 'line', title = 'Absolute Total Facet scores mean line plot', xticks = np.arange(1,4, step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[67]:
<matplotlib.legend.Legend at 0x26c514fc190>
No description has been provided for this image
In [68]:
# Absolute Facet scores mean line plot from low to high edu

df5.groupby('isced')[['Certainty_3','RiskComprehension_4','Numeracy_3','GraphLiteracy_3','Bayesianreasoning_1']].mean().plot( kind = 'line', title = 'Absolute Facet scores mean line plot', xticks = np.arange(1,4, step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[68]:
<matplotlib.legend.Legend at 0x26c510aea50>
No description has been provided for this image
In [69]:
# Normalised Facet scores mean line plot from low to high edu


df5.groupby('isced')[['Certainty_%','RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%','TotalScore_%']].mean().plot( kind = 'line', title = 'Normalised Facet scores mean line plot', xticks = np.arange(1,4, step = 1)).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[69]:
<matplotlib.legend.Legend at 0x26c52946790>
No description has been provided for this image
In [70]:
# Normalised mean scores for each facet stacked

df5.groupby('isced')[['Certainty_%','RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%']].mean().plot( kind = 'bar', title = 'Normalised mean scores for each facet stacked', stacked = True).legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[70]:
<matplotlib.legend.Legend at 0x26c518addd0>
No description has been provided for this image
In [71]:
# Normalised mean scores for each facet for each edu response category

df5.groupby('isced')[['Certainty_%','RiskComprehension_%','Numeracy_%','GraphLiteracy_%','Bayesianreasoning_%','TotalScore_%']].mean().T.plot(kind = 'bar', title = 'Normalised mean scores for each facet for each edu response category').legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
Out[71]:
<matplotlib.legend.Legend at 0x26c52a30550>
No description has been provided for this image
In [72]:
# Trend line for Absolute Total Facet Score vs edu (isced) reponses

sns.regplot (data = df5, x = 'isced', y = 'TotalScore_14')
Out[72]:
<Axes: xlabel='isced', ylabel='TotalScore_14'>
No description has been provided for this image
In [73]:
# Trend line for Absolute Independent Facet Score vs edu (isced) reponses

fig, ax6 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df5, x = 'isced', y = 'Certainty_3', fit_reg=True, ci=None, ax=ax6, label='Certainty_3')
sns.regplot (data = df5, x = 'isced', y = 'RiskComprehension_4', fit_reg=True, ci=None, ax=ax6, label='RiskComprehension_4')
sns.regplot (data = df5, x = 'isced', y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax6, label='Numeracy_3')
sns.regplot (data = df5, x = 'isced', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax6, label='GraphLiteracy_3')
sns.regplot (data = df5, x = 'isced', y = 'Bayesianreasoning_1',fit_reg=True, ci=None, ax=ax6, label='Bayesianreasoning_1' )

ax6.set(ylabel='Scores', xlabel='isced')
ax6.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [74]:
# Violine Plot for TotalScore_14 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'TotalScore_14')
Out[74]:
<Axes: xlabel='isced', ylabel='TotalScore_14'>
No description has been provided for this image
In [75]:
# Violine Plot for Certainty_3 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'Certainty_3')
Out[75]:
<Axes: xlabel='isced', ylabel='Certainty_3'>
No description has been provided for this image
In [76]:
# Violine Plot for RiskComprehension_4 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'RiskComprehension_4')
Out[76]:
<Axes: xlabel='isced', ylabel='RiskComprehension_4'>
No description has been provided for this image
In [77]:
# Violine Plot for GraphLiteracy_3 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'GraphLiteracy_3')
Out[77]:
<Axes: xlabel='isced', ylabel='GraphLiteracy_3'>
No description has been provided for this image
In [78]:
# Violine Plot for Numeracy_3 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'Numeracy_3')
Out[78]:
<Axes: xlabel='isced', ylabel='Numeracy_3'>
No description has been provided for this image
In [79]:
# Violine Plot for TotalScore_14 for each entry under edu.

sns.violinplot( data = df5, x = 'isced', y = 'Bayesianreasoning_1')
Out[79]:
<Axes: xlabel='isced', ylabel='Bayesianreasoning_1'>
No description has been provided for this image
In [80]:
# Descriptive stats for the data set, isced = 1
# NA values of wealth are removed

df6 = df5.dropna(axis = 0, subset = 'wealth')
# df6.drop(df6[df6['wealth'] == 3500000].index, inplace = True)
df6.loc[df6['isced']==1][['wealth']].describe()
Out[80]:
wealth
count 3.960000e+02
mean 3.082737e+05
std 6.153044e+05
min 0.000000e+00
25% 1.000000e+04
50% 8.750000e+04
75% 3.500000e+05
max 5.000000e+06
In [81]:
# Descriptive stats for the data set, isced = 2

df6.loc[df6['isced']==2][['wealth']].describe()
Out[81]:
wealth
count 4.770000e+02
mean 2.372361e+05
std 5.316798e+05
min 0.000000e+00
25% 3.500000e+03
50% 5.000000e+04
75% 2.200000e+05
max 5.000000e+06
In [82]:
# Descriptive stats for the data set, isced = 3

df6.loc[df6['isced']==3][['wealth']].describe()
Out[82]:
wealth
count 2.490000e+02
mean 1.428251e+05
std 5.596193e+05
min 0.000000e+00
25% 3.500000e+02
50% 5.000000e+03
75% 6.000000e+04
max 5.000000e+06
In [83]:
# Trend line for Absolute Tota Facet Score vs wealth reponses sorted by isced and ORDERED by wealth WITH scatter

fig, ax7 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'TotalScore_14', fit_reg=True, ci=None, ax=ax7, label='ISCED = 1')
sns.regplot (data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'TotalScore_14', fit_reg=True, ci=None, ax=ax7, label='ISCED = 2')
sns.regplot (data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'TotalScore_14', fit_reg=True, ci=None, ax=ax7, label='ISCED = 3')

ax7.set(ylabel='Total Scores_19', xlabel='Wealth')
ax7.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [84]:
# Trend line for Absolute Tota Facet Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax8 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'TotalScore_14', fit_reg=True, ci=None, ax=ax8, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'TotalScore_14', fit_reg=True, ci=None, ax=ax8, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'TotalScore_14', fit_reg=True, ci=None, ax=ax8, label='ISCED = 3')

ax8.set(ylabel='Total Scores_19', xlabel='Wealth')
ax8.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [85]:
# Trend line for Absolute Certainty Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax9 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'Certainty_3', fit_reg=True, ci=None, ax=ax9, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'Certainty_3', fit_reg=True, ci=None, ax=ax9, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'Certainty_3', fit_reg=True, ci=None, ax=ax9, label='ISCED = 3')

ax9.set(ylabel='Certainty_3', xlabel='Wealth')
ax9.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [86]:
# Trend line for Absolute Uncertainty Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax10 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'RiskComprehension_4', fit_reg=True, ci=None, ax=ax10, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'RiskComprehension_4', fit_reg=True, ci=None, ax=ax10, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'RiskComprehension_4', fit_reg=True, ci=None, ax=ax10, label='ISCED = 3')

ax10.set(ylabel='RiskComprehension_4', xlabel='Wealth')
ax10.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [87]:
# Trend line for Absolute Number Comprehension Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax11 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 3')

ax11.set(ylabel='Numeracy_3', xlabel='Wealth')
ax11.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [88]:
# Trend line for Absolute Graph Comprehension Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax11 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 3')

ax11.set(ylabel='GraphLiteracy_3', xlabel='Wealth')
ax11.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [89]:
# Trend line for Absolute Bayesian Reasoning Score vs wealth reponses sorted by isced and ORDERED by wealth WITHOUT scatter

fig, ax12 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df6.loc[df6['isced']==1].reset_index(drop = True), x = df6.loc[df6['isced']==1].reset_index(drop = True)['wealth'], y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 1')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==2].reset_index(drop = True), x = df6.loc[df6['isced']==2].reset_index(drop = True)['wealth'], y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 2')
sns.regplot (scatter = False, data = df6.loc[df6['isced']==3].reset_index(drop = True), x = df6.loc[df6['isced']==3].reset_index(drop = True)['wealth'], y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 3')

ax12.set(ylabel='Bayesianreasoning_1', xlabel='Wealth')
ax12.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [ ]:
In [ ]:
In [ ]:
In [90]:
# Descriptive stats for the data set, isced = 1
# NA values of income are removed

df7 = df5
df7.drop(df7[df7['income'] == 7].index, inplace = True)

df7.loc[df7['isced']==1].describe()
Out[90]:
responseid age age_rec isced income wealth Certainty_3 RiskComprehension_4 GraphLiteracy_3 Numeracy_3 Bayesianreasoning_1 Certainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_14 TotalScore_%
count 563.000000 563.000000 563.000000 563.0 563.000000 3.890000e+02 563.000000 563.000000 563.000000 563.000000 563.000000 563.000000 563.000000 563.000000 563.000000 563.000000 563.000000 563.000000
mean 5965.904085 44.307282 2.825933 1.0 3.454707 3.099350e+05 0.841918 3.140320 1.966252 2.069272 0.214920 28.063943 78.507993 65.541741 68.975725 21.492007 8.232682 58.804872
std 3904.222748 14.453745 0.719506 0.0 1.677566 6.198550e+05 1.061231 1.644529 0.964087 1.007356 0.411132 35.374367 41.113217 32.136239 33.578529 41.113217 3.015535 21.539534
min 5.000000 18.000000 2.000000 1.0 1.000000 0.000000e+00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 2413.000000 32.000000 2.000000 1.0 2.000000 1.000000e+04 0.000000 4.000000 1.000000 1.000000 0.000000 0.000000 100.000000 33.333333 33.333333 0.000000 6.000000 42.857143
50% 5869.000000 42.000000 3.000000 1.0 3.000000 8.500000e+04 0.000000 4.000000 2.000000 2.000000 0.000000 0.000000 100.000000 66.666667 66.666667 0.000000 9.000000 64.285714
75% 9093.000000 57.000000 3.000000 1.0 5.000000 3.500000e+05 1.000000 4.000000 3.000000 3.000000 0.000000 33.333333 100.000000 100.000000 100.000000 0.000000 10.000000 71.428571
max 15573.000000 75.000000 4.000000 1.0 6.000000 5.000000e+06 3.000000 4.000000 3.000000 3.000000 1.000000 100.000000 100.000000 100.000000 100.000000 100.000000 14.000000 100.000000
In [91]:
df7.loc[df7['isced']==2].describe()
Out[91]:
responseid age age_rec isced income wealth Certainty_3 RiskComprehension_4 GraphLiteracy_3 Numeracy_3 Bayesianreasoning_1 Certainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_14 TotalScore_%
count 733.000000 733.00000 733.000000 733.0 733.000000 4.680000e+02 733.000000 733.000000 733.000000 733.000000 733.000000 733.000000 733.000000 733.000000 733.000000 733.000000 733.000000 733.000000
mean 5960.547067 47.52251 3.012278 2.0 2.710778 2.389636e+05 0.631651 2.772169 1.817190 1.702592 0.180082 21.055025 69.304229 60.572988 56.753070 18.008186 7.103683 50.740596
std 4274.006749 15.05767 0.721347 0.0 1.514360 5.361378e+05 0.958529 1.846187 0.960635 1.052687 0.384518 31.950974 46.154663 32.021154 35.089552 38.451800 2.886306 20.616475
min 15.000000 18.00000 2.000000 2.0 1.000000 0.000000e+00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 1618.000000 35.00000 2.000000 2.0 1.000000 3.375000e+03 0.000000 0.000000 1.000000 1.000000 0.000000 0.000000 0.000000 33.333333 33.333333 0.000000 5.000000 35.714286
50% 6003.000000 49.00000 3.000000 2.0 2.000000 5.000000e+04 0.000000 4.000000 2.000000 2.000000 0.000000 0.000000 100.000000 66.666667 66.666667 0.000000 8.000000 57.142857
75% 9364.000000 60.00000 4.000000 2.0 4.000000 2.275000e+05 1.000000 4.000000 3.000000 3.000000 0.000000 33.333333 100.000000 100.000000 100.000000 0.000000 9.000000 64.285714
max 14419.000000 75.00000 4.000000 2.0 6.000000 5.000000e+06 3.000000 4.000000 3.000000 3.000000 1.000000 100.000000 100.000000 100.000000 100.000000 100.000000 14.000000 100.000000
In [92]:
df7.loc[df7['isced']==3].describe()
Out[92]:
responseid age age_rec isced income wealth Certainty_3 RiskComprehension_4 GraphLiteracy_3 Numeracy_3 Bayesianreasoning_1 Certainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_14 TotalScore_%
count 404.000000 404.000000 404.000000 404.0 404.000000 2.470000e+02 404.000000 404.000000 404.000000 404.000000 404.000000 404.000000 404.000000 404.000000 404.000000 404.000000 404.000000 404.000000
mean 3369.396040 50.579208 3.158416 3.0 2.237624 1.439812e+05 0.670792 2.633663 1.547030 1.448020 0.165842 22.359736 65.841584 51.567657 48.267327 16.584158 6.465347 46.181047
std 3465.118524 15.985296 0.745310 0.0 1.322818 5.617409e+05 0.977686 1.899316 1.028268 0.988656 0.372400 32.589524 47.482891 34.275616 32.955192 37.239955 3.087040 22.050283
min 12.000000 18.000000 2.000000 3.0 1.000000 0.000000e+00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 906.500000 39.750000 3.000000 3.0 1.000000 4.000000e+02 0.000000 0.000000 1.000000 1.000000 0.000000 0.000000 0.000000 33.333333 33.333333 0.000000 4.000000 28.571429
50% 1903.000000 54.000000 3.000000 3.0 2.000000 5.000000e+03 0.000000 4.000000 2.000000 1.000000 0.000000 0.000000 100.000000 66.666667 33.333333 0.000000 7.000000 50.000000
75% 5277.750000 63.000000 4.000000 3.0 3.000000 6.000000e+04 1.000000 4.000000 2.000000 2.000000 0.000000 33.333333 100.000000 66.666667 66.666667 0.000000 9.000000 64.285714
max 15636.000000 75.000000 4.000000 3.0 6.000000 5.000000e+06 3.000000 4.000000 3.000000 3.000000 1.000000 100.000000 100.000000 100.000000 100.000000 100.000000 13.000000 92.857143
In [93]:
# Trend line for Absolute Tota Facet Score vs income reponses sorted by isced and ORDERED by income WITH scatter

fig, ax7 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = df7.loc[df7['isced']==1]['TotalScore_14'], fit_reg=True, ci=None, ax=ax7, label='ISCED = 1')
sns.regplot (data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = df7.loc[df7['isced']==2]['TotalScore_14'], fit_reg=True, ci=None, ax=ax7, label='ISCED = 2')
sns.regplot (data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = df7.loc[df7['isced']==3]['TotalScore_14'], fit_reg=True, ci=None, ax=ax7, label='ISCED = 3')

ax7.set(ylabel='Total Scores_19', xlabel='INCOME')
ax7.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [94]:
# Trend line for Absolute Tota Facet Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax8 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = 'TotalScore_14', fit_reg=True, ci=None, ax=ax8, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = 'TotalScore_14', fit_reg=True, ci=None, ax=ax8, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = 'TotalScore_14', fit_reg=True, ci=None, ax=ax8, label='ISCED = 3')

ax8.set(ylabel='Total Scores_19', xlabel='INCOME')
ax8.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [95]:
# Trend line for Absolute Certainty Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax9 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = 'Certainty_3', fit_reg=True, ci=None, ax=ax9, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = 'Certainty_3', fit_reg=True, ci=None, ax=ax9, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = 'Certainty_3', fit_reg=True, ci=None, ax=ax9, label='ISCED = 3')

ax9.set(ylabel='Certainty_3', xlabel='INCOME')
ax9.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
In [96]:
# Trend line for Absolute Risk Comprehension Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax10 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = 'RiskComprehension_4', fit_reg=True, ci=None, ax=ax10, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = 'RiskComprehension_4', fit_reg=True, ci=None, ax=ax10, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = 'RiskComprehension_4', fit_reg=True, ci=None, ax=ax10, label='ISCED = 3')

ax10.set(ylabel='RiskComprehension_4', xlabel='INCOME')
ax10.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [97]:
# Trend line for Absolute Number Comprehension Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax11 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = 'Numeracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 3')

ax11.set(ylabel='Numeracy_3', xlabel='INCOME')
ax11.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [98]:
# Trend line for Absolute Graph Comprehension Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax11 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = 'GraphLiteracy_3', fit_reg=True, ci=None, ax=ax11, label='ISCED = 3')

ax11.set(ylabel='GraphLiteracy_3', xlabel='INCOME')
ax11.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [99]:
# Trend line for Absolute Bayesian Reasoning Score vs income reponses sorted by isced and ORDERED by income WITHOUT scatter

fig, ax12 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[df7['isced']==1].reset_index(drop = True), x = 'income', y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==2].reset_index(drop = True), x = 'income', y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[df7['isced']==3].reset_index(drop = True), x = 'income', y = 'Bayesianreasoning_1', fit_reg=True, ci=None, ax=ax12, label='ISCED = 3')

ax12.set(ylabel='Bayesianreasoning_1', xlabel='INCOME')
ax12.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [ ]:
In [ ]:
In [ ]:
In [100]:
df7
Out[100]:
responseid age age_rec isced income wealth Certainty_3 RiskComprehension_4 GraphLiteracy_3 Numeracy_3 Bayesianreasoning_1 Certainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_14 TotalScore_%
0 10237 42 3 1 6 50000.0 1 4 2 3 0 33.333333 100.0 66.666667 100.000000 0.0 10 71.428571
1 32 31 2 1 2 NaN 2 4 2 3 0 66.666667 100.0 66.666667 100.000000 0.0 11 78.571429
2 5234 28 2 1 2 NaN 0 4 2 3 0 0.000000 100.0 66.666667 100.000000 0.0 9 64.285714
3 118 33 2 1 1 120000.0 0 0 1 0 0 0.000000 0.0 33.333333 0.000000 0.0 1 7.142857
4 2842 26 2 1 3 200000.0 0 4 2 3 0 0.000000 100.0 66.666667 100.000000 0.0 9 64.285714
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1948 1456 36 3 3 1 NaN 2 4 2 2 1 66.666667 100.0 66.666667 66.666667 100.0 11 78.571429
1949 2846 47 3 3 2 200000.0 0 0 1 0 0 0.000000 0.0 33.333333 0.000000 0.0 1 7.142857
1950 8284 38 3 3 1 450000.0 0 0 1 0 0 0.000000 0.0 33.333333 0.000000 0.0 1 7.142857
1951 8253 50 3 3 1 0.0 0 0 1 2 1 0.000000 0.0 33.333333 66.666667 100.0 4 28.571429
1952 2671 73 4 3 3 1200.0 0 4 3 1 0 0.000000 100.0 100.000000 33.333333 0.0 8 57.142857

1700 rows × 18 columns

In [101]:
# Since we already have a classification for Age groups in the form of age_rec, we will use that.
# We will also use median of age to see if it yields any relevant results, as instructed.

# AXES to be used = Age or age groups, ISCED, Income

# Age groups = [2,3,4]
# Age group 2 = 18 to 35 y/o
# Age group 3 = 36 to 59 y/o
# Age group 4 = 60 to 75 y/o (75 y/o, i.e, within the scope of the data we have, it can mean 60 and above also)
In [102]:
df7.loc[(df7['age_rec']==2)].describe()
Out[102]:
responseid age age_rec isced income wealth Certainty_3 RiskComprehension_4 GraphLiteracy_3 Numeracy_3 Bayesianreasoning_1 Certainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_14 TotalScore_%
count 474.000000 474.000000 474.0 474.000000 474.000000 2.990000e+02 474.000000 474.000000 474.000000 474.000000 474.000000 474.000000 474.000000 474.000000 474.000000 474.000000 474.000000 474.000000
mean 6493.943038 27.594937 2.0 1.751055 2.729958 1.875135e+05 0.907173 2.936709 1.751055 1.721519 0.200422 30.239100 73.417722 58.368495 57.383966 20.042194 7.516878 53.691983
std 4297.087794 5.150223 0.0 0.739442 1.541144 5.651190e+05 1.074316 1.768948 1.036974 1.083453 0.400739 35.810545 44.223708 34.565815 36.115094 40.073905 3.139587 22.425624
min 24.000000 18.000000 2.0 1.000000 1.000000 0.000000e+00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 2203.500000 23.250000 2.0 1.000000 1.000000 1.000000e+03 0.000000 0.000000 1.000000 1.000000 0.000000 0.000000 0.000000 33.333333 33.333333 0.000000 5.000000 35.714286
50% 6407.500000 29.000000 2.0 2.000000 2.000000 2.000000e+04 1.000000 4.000000 2.000000 2.000000 0.000000 33.333333 100.000000 66.666667 66.666667 0.000000 8.000000 57.142857
75% 9870.250000 32.000000 2.0 2.000000 4.000000 1.500000e+05 2.000000 4.000000 3.000000 3.000000 0.000000 66.666667 100.000000 100.000000 100.000000 0.000000 10.000000 71.428571
max 14155.000000 35.000000 2.0 3.000000 6.000000 5.000000e+06 3.000000 4.000000 3.000000 3.000000 1.000000 100.000000 100.000000 100.000000 100.000000 100.000000 14.000000 100.000000
In [103]:
df7.loc[(df7['age_rec']==3)].describe()
Out[103]:
responseid age age_rec isced income wealth Certainty_3 RiskComprehension_4 GraphLiteracy_3 Numeracy_3 Bayesianreasoning_1 Certainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_14 TotalScore_%
count 777.000000 777.000000 777.0 777.000000 777.000000 4.970000e+02 777.000000 777.000000 777.000000 777.000000 777.000000 777.000000 777.000000 777.000000 777.000000 777.000000 777.000000 777.000000
mean 4701.234234 48.464607 3.0 1.890605 2.989704 2.408083e+05 0.709138 2.867439 1.846847 1.782497 0.175032 23.637924 71.685972 61.561562 59.416559 17.503218 7.380952 52.721088
std 3775.089082 7.112251 0.0 0.731914 1.654390 5.123195e+05 1.018049 1.803257 0.979674 1.041808 0.380239 33.934973 45.081418 32.655803 34.726924 38.023939 3.062333 21.873808
min 5.000000 36.000000 3.0 1.000000 1.000000 0.000000e+00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 1086.000000 42.000000 3.0 1.000000 2.000000 2.500000e+03 0.000000 0.000000 1.000000 1.000000 0.000000 0.000000 0.000000 33.333333 33.333333 0.000000 5.000000 35.714286
50% 3166.000000 49.000000 3.0 2.000000 3.000000 5.000000e+04 0.000000 4.000000 2.000000 2.000000 0.000000 0.000000 100.000000 66.666667 66.666667 0.000000 8.000000 57.142857
75% 8497.000000 55.000000 3.0 2.000000 4.000000 2.500000e+05 1.000000 4.000000 3.000000 3.000000 0.000000 33.333333 100.000000 100.000000 100.000000 0.000000 10.000000 71.428571
max 15573.000000 59.000000 3.0 3.000000 6.000000 4.500000e+06 3.000000 4.000000 3.000000 3.000000 1.000000 100.000000 100.000000 100.000000 100.000000 100.000000 14.000000 100.000000
In [104]:
df7.loc[(df7['age_rec']==4)].describe()
Out[104]:
responseid age age_rec isced income wealth Certainty_3 RiskComprehension_4 GraphLiteracy_3 Numeracy_3 Bayesianreasoning_1 Certainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_14 TotalScore_%
count 449.000000 449.000000 449.0 449.000000 449.000000 3.080000e+02 449.000000 449.000000 449.000000 449.000000 449.000000 449.000000 449.000000 449.000000 449.000000 449.000000 449.000000 449.000000
mean 5251.966592 65.648107 4.0 2.097996 2.714922 2.993987e+05 0.505568 2.770601 1.779510 1.775056 0.198218 16.852264 69.265033 59.317001 59.168523 19.821826 7.028953 50.206809
std 4253.142346 4.150732 0.0 0.746552 1.537848 6.701023e+05 0.842822 1.847641 0.955683 1.026741 0.399102 28.094056 46.191021 31.856087 34.224712 39.910216 2.930986 20.935613
min 15.000000 60.000000 4.0 1.000000 1.000000 0.000000e+00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 1333.000000 62.000000 4.0 2.000000 1.000000 5.000000e+03 0.000000 0.000000 1.000000 1.000000 0.000000 0.000000 0.000000 33.333333 33.333333 0.000000 5.000000 35.714286
50% 5256.000000 65.000000 4.0 2.000000 2.000000 5.000000e+04 0.000000 4.000000 2.000000 2.000000 0.000000 0.000000 100.000000 66.666667 66.666667 0.000000 7.000000 50.000000
75% 8817.000000 69.000000 4.0 3.000000 4.000000 3.000000e+05 1.000000 4.000000 3.000000 3.000000 0.000000 33.333333 100.000000 100.000000 100.000000 0.000000 9.000000 64.285714
max 15636.000000 75.000000 4.0 3.000000 6.000000 5.000000e+06 3.000000 4.000000 3.000000 3.000000 1.000000 100.000000 100.000000 100.000000 100.000000 100.000000 14.000000 100.000000
In [ ]:
In [105]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['TotalScore_14'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['TotalScore_14'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['TotalScore_14'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Total Scores_14', xlabel='INCOME 18 to 35')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['TotalScore_14'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['TotalScore_14'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['TotalScore_14'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Total Scores_14', xlabel='INCOME 36 to 59')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['TotalScore_14'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['TotalScore_14'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['TotalScore_14'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Total Scores_14', xlabel='INCOME 60 to 75')
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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In [106]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['TotalScore_14'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['TotalScore_14'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['TotalScore_14'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==2  ].groupby(['income']).mean(numeric_only=True)['TotalScore_14'], yerr = df7.loc[ (df7['age_rec']==2) ].groupby(['income']).sem(ddof = 1)['TotalScore_14'], fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Total Scores_14', xlabel='INCOME 18 to 35',  yticks = np.arange(5, 16 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['TotalScore_14'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['TotalScore_14'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['TotalScore_14'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Total Scores_14', xlabel='INCOME 36 to 59',  yticks = np.arange(5, 16 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==3  ].groupby(['income']).mean(numeric_only=True)['TotalScore_14'], yerr = df7.loc[ (df7['age_rec']==3) ].groupby(['income']).sem()['TotalScore_14'], fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['TotalScore_14'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['TotalScore_14'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['TotalScore_14'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Total Scores_14', xlabel='INCOME 60 to 75',  yticks = np.arange(5, 16 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==4  ].groupby(['income']).mean(numeric_only=True)['TotalScore_14'], yerr = df7.loc[ (df7['age_rec']==4) ].groupby(['income']).sem()['TotalScore_14'], fmt='o', color = lighten_color('gray',0.5))
plt.show()
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In [107]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['Certainty_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['Certainty_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['Certainty_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Certainty_3', xlabel='INCOME 18 to 35',  yticks = np.arange(0, 6 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==2  ].groupby(['income']).mean()['Certainty_3'], yerr = df7.loc[ (df7['age_rec']==2) ].groupby(['income']).sem()['Certainty_3'], fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['Certainty_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['Certainty_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['Certainty_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Certainty_3', xlabel='INCOME 36 to 59',  yticks = np.arange(0, 6 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==3  ].groupby(['income']).mean()['Certainty_3'], yerr = df7.loc[ (df7['age_rec']==3) ].groupby(['income']).sem()['Certainty_3'], fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['Certainty_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['Certainty_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['Certainty_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Certainty_3', xlabel='INCOME 60 to 75',  yticks = np.arange(0, 6 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==4  ].groupby(['income']).mean()['Certainty_3'], yerr = df7.loc[ (df7['age_rec']==4) ].groupby(['income']).sem()['Certainty_3'], fmt='o', color = lighten_color('gray',0.5))
plt.show()
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In [108]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['RiskComprehension_4'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['RiskComprehension_4'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['RiskComprehension_4'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='RiskComprehension_4', xlabel='INCOME 18 to 35',  yticks = np.arange(0, 3 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==2  ].groupby(['income']).mean()['RiskComprehension_4'], yerr = df7.loc[ (df7['age_rec']==2) ].groupby(['income']).sem()['RiskComprehension_4'], fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['RiskComprehension_4'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['RiskComprehension_4'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['RiskComprehension_4'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='RiskComprehension_4', xlabel='INCOME 36 to 59',  yticks = np.arange(0, 3 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==3  ].groupby(['income']).mean()['RiskComprehension_4'], yerr = df7.loc[ (df7['age_rec']==3) ].groupby(['income']).sem()['RiskComprehension_4'], fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['RiskComprehension_4'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['RiskComprehension_4'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['RiskComprehension_4'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='RiskComprehension_4', xlabel='INCOME 60 to 75',  yticks = np.arange(0, 3 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==4  ].groupby(['income']).mean()['RiskComprehension_4'], yerr = df7.loc[ (df7['age_rec']==4) ].groupby(['income']).sem()['RiskComprehension_4'], fmt='o', color = lighten_color('gray',0.5))
plt.show()
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In [109]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Numeracy_3', xlabel='INCOME 18 to 35',  yticks = np.arange(0, 6 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==2  ].groupby(['income']).mean()['Numeracy_3'], yerr = df7.loc[ (df7['age_rec']==2) ].groupby(['income']).sem()['Numeracy_3'], fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Numeracy_3', xlabel='INCOME 36 to 59',  yticks = np.arange(0, 6 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==3  ].groupby(['income']).mean()['Numeracy_3'], yerr = df7.loc[ (df7['age_rec']==3) ].groupby(['income']).sem()['Numeracy_3'], fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Numeracy_3', xlabel='INCOME 60 to 75',  yticks = np.arange(0, 6 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==4  ].groupby(['income']).mean()['Numeracy_3'], yerr = df7.loc[ (df7['age_rec']==4) ].groupby(['income']).sem()['Numeracy_3'], fmt='o', color = lighten_color('gray',0.5))
plt.show()
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In [110]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='GraphLiteracy_3', xlabel='INCOME 18 to 35',  yticks = np.arange(0, 4 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==2  ].groupby(['income']).mean()['GraphLiteracy_3'], yerr = df7.loc[ (df7['age_rec']==2) ].groupby(['income']).sem()['GraphLiteracy_3'], fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='GraphLiteracy_3', xlabel='INCOME 36 to 59',  yticks = np.arange(0, 4 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==3  ].groupby(['income']).mean()['GraphLiteracy_3'], yerr = df7.loc[ (df7['age_rec']==3) ].groupby(['income']).sem()['GraphLiteracy_3'], fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='GraphLiteracy_3', xlabel='INCOME 60 to 75',  yticks = np.arange(0, 4 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==4  ].groupby(['income']).mean()['GraphLiteracy_3'], yerr = df7.loc[ (df7['age_rec']==4) ].groupby(['income']).sem()['GraphLiteracy_3'], fmt='o', color = lighten_color('gray',0.5))
plt.show()
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In [111]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==2) & (df7['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Bayesianreasoning_1', xlabel='INCOME 18 to 35',  yticks = np.arange(0, 5 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax1.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==2  ].groupby(['income']).mean()['Bayesianreasoning_1'], yerr = df7.loc[ (df7['age_rec']==2) ].groupby(['income']).sem()['Bayesianreasoning_1'], fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==3) & (df7['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Bayesianreasoning_1', xlabel='INCOME 36 to 59',  yticks = np.arange(0, 5 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax2.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==3  ].groupby(['income']).mean()['Bayesianreasoning_1'], yerr = df7.loc[ (df7['age_rec']==3) ].groupby(['income']).sem()['Bayesianreasoning_1'], fmt='o', color = lighten_color('gray',0.5))
plt.show()

fig, ax3 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age_rec']==4) & (df7['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax3, label='ISCED = 3')

ax3.set(ylabel='Bayesianreasoning_1', xlabel='INCOME 60 to 75',  yticks = np.arange(0, 5 , 1))
ax3.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
ax3.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ df7['age_rec']==4  ].groupby(['income']).mean()['Bayesianreasoning_1'], yerr = df7.loc[ (df7['age_rec']==4) ].groupby(['income']).sem()['Bayesianreasoning_1'], fmt='o', color = lighten_color('gray',0.5))
plt.show()
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In [112]:
# Age median method
# Age_median
age_m = df7['age'].median()
age_m
Out[112]:
48.0
In [113]:
df7.loc[(df7['age'] < age_m)].describe()
Out[113]:
responseid age age_rec isced income wealth Certainty_3 RiskComprehension_4 GraphLiteracy_3 Numeracy_3 Bayesianreasoning_1 Certainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_14 TotalScore_%
count 819.000000 819.000000 819.000000 819.000000 819.000000 5.150000e+02 819.000000 819.000000 819.000000 819.000000 819.000000 819.000000 819.000000 819.000000 819.000000 819.000000 819.000000 819.000000
mean 5794.545788 33.468864 2.421245 1.764347 2.848596 1.899709e+05 0.865690 2.920635 1.804640 1.778999 0.192918 28.856329 73.015873 60.154660 59.299959 19.291819 7.562882 54.020583
std 4131.602460 8.223049 0.494060 0.731344 1.593548 4.842720e+05 1.084017 1.776594 1.024601 1.063393 0.394830 36.133899 44.414842 34.153368 35.446444 39.483048 3.135395 22.395675
min 9.000000 18.000000 2.000000 1.000000 1.000000 0.000000e+00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 1735.500000 27.500000 2.000000 1.000000 1.000000 1.000000e+03 0.000000 0.000000 1.000000 1.000000 0.000000 0.000000 0.000000 33.333333 33.333333 0.000000 5.000000 35.714286
50% 5766.000000 34.000000 2.000000 2.000000 3.000000 3.000000e+04 0.000000 4.000000 2.000000 2.000000 0.000000 0.000000 100.000000 66.666667 66.666667 0.000000 8.000000 57.142857
75% 9413.500000 40.000000 3.000000 2.000000 4.000000 1.800000e+05 1.000000 4.000000 3.000000 3.000000 0.000000 33.333333 100.000000 100.000000 100.000000 0.000000 10.000000 71.428571
max 14155.000000 47.000000 3.000000 3.000000 6.000000 5.000000e+06 3.000000 4.000000 3.000000 3.000000 1.000000 100.000000 100.000000 100.000000 100.000000 100.000000 14.000000 100.000000
In [114]:
df7.loc[(df7['age'] > age_m)].describe()
Out[114]:
responseid age age_rec isced income wealth Certainty_3 RiskComprehension_4 GraphLiteracy_3 Numeracy_3 Bayesianreasoning_1 Certainty_% RiskComprehension_% GraphLiteracy_% Numeracy_% Bayesianreasoning_% TotalScore_14 TotalScore_%
count 845.000000 845.000000 845.000000 845.000000 845.000000 5.660000e+02 845.000000 845.000000 845.000000 845.000000 845.000000 845.000000 845.000000 845.000000 845.000000 845.000000 845.000000 845.000000
mean 4952.375148 60.442604 3.531361 2.037870 2.837870 2.856130e+05 0.556213 2.816568 1.804734 1.742012 0.188166 18.540434 70.414201 60.157791 58.067061 18.816568 7.107692 50.769231
std 4068.956618 6.693923 0.499311 0.738889 1.597832 6.408081e+05 0.886029 1.826791 0.961819 1.035941 0.391076 29.534297 45.669779 32.060638 34.531365 39.107589 2.958080 21.129142
min 5.000000 49.000000 3.000000 1.000000 1.000000 0.000000e+00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 1163.000000 55.000000 3.000000 1.000000 1.000000 4.000000e+03 0.000000 0.000000 1.000000 1.000000 0.000000 0.000000 0.000000 33.333333 33.333333 0.000000 5.000000 35.714286
50% 3052.000000 60.000000 4.000000 2.000000 3.000000 5.000000e+04 0.000000 4.000000 2.000000 2.000000 0.000000 0.000000 100.000000 66.666667 66.666667 0.000000 8.000000 57.142857
75% 8620.000000 65.000000 4.000000 3.000000 4.000000 3.000000e+05 1.000000 4.000000 3.000000 3.000000 0.000000 33.333333 100.000000 100.000000 100.000000 0.000000 9.000000 64.285714
max 15636.000000 75.000000 4.000000 3.000000 6.000000 5.000000e+06 3.000000 4.000000 3.000000 3.000000 1.000000 100.000000 100.000000 100.000000 100.000000 100.000000 14.000000 100.000000
In [ ]:
In [115]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['TotalScore_14'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['TotalScore_14'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['TotalScore_14'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.set(ylabel='Total Scores_19', xlabel='INCOME for < median age')
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (data = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)]['TotalScore_14'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (data = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)]['TotalScore_14'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (data = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)]['TotalScore_14'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.set(ylabel='Total Scores_19', xlabel='INCOME for > median age')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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No description has been provided for this image
In [116]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['TotalScore_14'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['TotalScore_14'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['TotalScore_14'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ (df7['age'] < age_m)  ].groupby(['income']).mean()['TotalScore_14'], yerr = df7.loc[ (df7['age'] < age_m) ].groupby(['income']).sem()['TotalScore_14'], fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Total Scores_19', xlabel='INCOME for < median age',  yticks = np.arange(5, 16 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)]['TotalScore_14'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)]['TotalScore_14'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)]['TotalScore_14'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ (df7['age'] > age_m)  ].groupby(['income']).mean()['TotalScore_14'], yerr = df7.loc[ (df7['age'] > age_m) ].groupby(['income']).sem()['TotalScore_14'], fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='Total Scores_19', xlabel='INCOME for > median age',  yticks = np.arange(5, 16 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
No description has been provided for this image
In [117]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['Certainty_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['Certainty_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['Certainty_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ (df7['age'] < age_m)  ].groupby(['income']).mean()['Certainty_3'], yerr = df7.loc[ (df7['age'] < age_m) ].groupby(['income']).sem()['Certainty_3'], fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Certainty_3', xlabel='INCOME for < median age',  yticks = np.arange(0, 6 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)]['Certainty_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)]['Certainty_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)]['Certainty_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ (df7['age'] > age_m)  ].groupby(['income']).mean()['Certainty_3'], yerr = df7.loc[ (df7['age'] > age_m) ].groupby(['income']).sem()['Certainty_3'], fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='Certainty_3', xlabel='INCOME for > median age')
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
No description has been provided for this image
In [118]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['RiskComprehension_4'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['RiskComprehension_4'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['RiskComprehension_4'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ (df7['age'] < age_m)  ].groupby(['income']).mean()['RiskComprehension_4'], yerr = df7.loc[ (df7['age'] < age_m) ].groupby(['income']).sem()['RiskComprehension_4'], fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='RiskComprehension_4', xlabel='INCOME for < median age',  yticks = np.arange(0, 3 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)]['RiskComprehension_4'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)]['RiskComprehension_4'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)]['RiskComprehension_4'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ (df7['age'] > age_m)  ].groupby(['income']).mean()['RiskComprehension_4'], yerr = df7.loc[ (df7['age'] > age_m) ].groupby(['income']).sem()['RiskComprehension_4'], fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='RiskComprehension_4', xlabel='INCOME for > median age',  yticks = np.arange(0, 3 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
No description has been provided for this image
In [119]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ (df7['age'] < age_m)  ].groupby(['income']).mean()['Numeracy_3'], yerr = df7.loc[ (df7['age'] < age_m) ].groupby(['income']).sem()['Numeracy_3'], fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Numeracy_3', xlabel='INCOME for < median age',  yticks = np.arange(0, 6 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)]['Numeracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ (df7['age'] > age_m)  ].groupby(['income']).mean()['Numeracy_3'], yerr = df7.loc[ (df7['age'] > age_m) ].groupby(['income']).sem()['Numeracy_3'], fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='Numeracy_3', xlabel='INCOME for > median age',  yticks = np.arange(0, 6 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
No description has been provided for this image
In [120]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ (df7['age'] < age_m)  ].groupby(['income']).mean()['GraphLiteracy_3'], yerr = df7.loc[ (df7['age'] < age_m) ].groupby(['income']).sem()['GraphLiteracy_3'], fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='GraphLiteracy_3', xlabel='INCOME for < median age',  yticks = np.arange(0, 4 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)]['GraphLiteracy_3'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ (df7['age'] > age_m)  ].groupby(['income']).mean()['GraphLiteracy_3'], yerr = df7.loc[ (df7['age'] > age_m) ].groupby(['income']).sem()['GraphLiteracy_3'], fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='GraphLiteracy_3', xlabel='INCOME for > median age',  yticks = np.arange(0, 4 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
No description has been provided for this image
No description has been provided for this image
In [121]:
fig, ax1 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] < age_m) & (df7['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax1, label='ISCED = 3')

ax1.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ (df7['age'] < age_m)  ].groupby(['income']).mean()['Bayesianreasoning_1'], yerr = df7.loc[ (df7['age'] < age_m) ].groupby(['income']).sem()['Bayesianreasoning_1'], fmt='o', color = lighten_color('gray',0.5))
ax1.set(ylabel='Bayesianreasoning_1', xlabel='INCOME for < median age',  yticks = np.arange(0, 5 , 1))
ax1.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()

fig, ax2 = plt.subplots(figsize=(6, 6))

sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==1)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 1')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==2)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 2')
sns.regplot (scatter = False, data = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)].reset_index(drop = True), x = 'income', y = df7.loc[(df7['age'] > age_m) & (df7['isced']==3)]['Bayesianreasoning_1'], fit_reg=True, ci=None, ax=ax2, label='ISCED = 3')

ax2.errorbar(x = [1,2,3,4,5,6], y = df7.loc[ (df7['age'] > age_m)  ].groupby(['income']).mean()['Bayesianreasoning_1'], yerr = df7.loc[ (df7['age'] > age_m) ].groupby(['income']).sem()['Bayesianreasoning_1'], fmt='o', color = lighten_color('gray',0.5))
ax2.set(ylabel='Bayesianreasoning_1', xlabel='INCOME for > median age',  yticks = np.arange(0, 5 , 1))
ax2.legend(loc='center left',bbox_to_anchor=(1.0, 0.5))
plt.show()
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